Meta Finishes Migration of Petabyte-Scale Data Ingestion System
Meta has successfully migrated its entire data ingestion system to a new architecture, handling petabytes of social graph data daily. The move from a legacy system to a self-managed warehouse service ensures greater reliability and efficiency for analytics, machine learning, and product development.
“This was a massive undertaking, involving thousands of jobs and petabytes of data,” said Sarah Chen, Meta’s engineering lead for data infrastructure. “We prioritized data integrity and operational stability throughout the migration.” The transition affected all teams relying on up-to-date snapshots of the social graph for day-to-day decisions.
Background: Why Meta Needed a New Ingestion Architecture
Meta’s social graph is powered by one of the world’s largest MySQL deployments. The legacy system used customer-owned pipelines that worked well at smaller scales but showed instability under strict data landing time requirements as the company grew.
“The old system simply couldn’t keep up with the increasing demand for fresh data,” Chen explained. “We needed a simpler, self-managed service that could operate efficiently at hyperscale without sacrificing reliability.” The new architecture eliminates customer-owned pipelines and centralizes ingestion into a unified warehouse service.
The Migration Challenge
Migrating a system of this size presented major challenges. The team had to ensure seamless transitions for thousands of individual jobs while implementing robust rollout and rollback controls to handle any issues.
“Every job had to be verified for correctness before moving to the next stage,” Chen noted. “We couldn’t afford any data quality issues or latency regressions.” The migration lifecycle involved strict criteria: no data quality differences, no landing latency regressions, and no resource utilization regressions.
Verification Process
Each job underwent rigorous verification. The team compared row counts and checksums to ensure complete consistency between old and new systems. Additionally, they monitored landing latency and resource usage to confirm the new system matched or outperformed the old one.
“We set up automated checks that would halt the migration if any criteria weren’t met,” said Chen. “This allowed us to identify and resolve issues early, maintaining data integrity throughout the process.”
What This Means for Meta’s Operations
The new architecture enhances reliability and efficiency for downstream data products. Teams across Meta—from analysts training machine learning models to engineers developing new features—now depend on a more stable ingestion pipeline.
“This migration ensures that our analytics and reporting remain timely and accurate, even as our data volumes continue to grow,” Chen emphasized. The move also simplifies maintenance, as the self-managed service reduces the complexity of managing customer-owned pipelines.
Key benefits include:
- Improved reliability: The new system handles peak loads without instability.
- Faster data landing: Latency improvements ensure fresher data for real-time decisions.
- Resource efficiency: Reduced overhead from centralized management.
Meta has deprecated the legacy system entirely, now running 100% of its data ingestion workload on the new architecture. The company plans to share more detailed migration strategies and architectural decisions in upcoming technical publications.
“We want other teams facing similar challenges to learn from our experience,” Chen concluded. “A methodical lifecycle approach, with clear success criteria and automated verification, was key to our success.”